This study analyzes the effects of image noise on pose estimation error to meet the requirements of high-accuracy pose measurements in high-speed complex flow fields. A monocular pose estimation optimization algorithm of adaptive reprojection error is proposed based on the traditional nonlinear optimization pose algorithm. In the algorithm, the initial value of pose estimation is set as the center, wherein the constraint interval is set. A new penalty function is formulated to transform the constrained nonlinear optimization into an unconstrained nonlinear optimization. The relation between the image reprojection error and constraint interval is analyzed; then, the corresponding mathematical model is built based on the analysis results. The constraint interval is automatically adjusted according to the model to optimize the parameters of pose estimation for the constrained nonlinear adaptive reprojection error. The simulation results reveal that the proposed algorithm provides an optimal solution for the reprojection error and pose estimation parameters under different image noise levels. The proposed algorithm is superior to the traditional nonlinear optimization algorithm and has higher pose estimation accuracy.